Table 1 Cross-validated prediction accuracies and standard errors of three genomic selection models (genomic best linear unbiased prediction with additive relationship matrix (G-BLUP), extended G-BLUP with additive and additive × additive relationship matrices (EG-BLUP), and reproducing kernel Hilbert space regression based on the Gaussian kernel (RKHS)] in four data sets
Data setTrait–environmenteG-BLUPEG-BLUPRKHS
Wheat_1aGY_E10.505 ± 0.0340.571 ± 0.0290.576 ± 0.033
GY_E20.493 ± 0.0340.500 ± 0.0340.499 ± 0.034
GY_E30.379 ± 0.0410.421 ± 0.0350.428 ± 0.034
GY_E40.484 ± 0.0330.525 ± 0.0290.526 ± 0.034
Wheat_2bGY_drought0.435 ± 0.0580.445 ± 0.0560.444 ± 0.054
GY_irrigated0.537 ± 0.0460.550 ± 0.0460.556 ± 0.042
Maize_1cGY_drought0.429 ± 0.0440.440 ± 0.0450.449 ± 0.043
GY_irrigated0.537 ± 0.0380.546 ± 0.0370.544 ± 0.037
Maize_2d dentDMY0.632 ± 0.0300.627 ± 0.0310.619 ± 0.032
Maize_2d flintDMY0.651 ± 0.0200.649 ± 0.0210.643 ± 0.021
  • The highest prediction accuracy for each trait in each data set is underlined.

  • a Data set previously described in Crossa et al. (2010); 599 lines and 1447 DArT markers were used.

  • b Data set previously described in Poland et al. (2012); 254 lines and 1576 SNP markers were used.

  • c Data set previously described in Crossa et al. (2010); 264 lines and 1135 SNP markers were used.

  • d Data set previously described in Bauer et al. (2013) and Lehermeier et al. (2014); 847 genotypes and 31,498 SNP markers were used for dent lines and 833 genotypes and 29,466 SNP markers were used for flint lines.

  • e GY, grain yield; DMY, dry matter yield.